This repository presents a comprehensive analysis of fake news detection leveraging natural language processing (NLP) techniques. The analysis is conducted on a labeled dataset of news articles, where each article is categorized as either fake or real. The primary goal is to employ various methodologies, including data preprocessing, exploratory data analysis (EDA), vectorization, and the implementation of multiple classification models, to discern patterns and evaluate the effectiveness of different techniques in distinguishing between authentic and fabricated news.
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Data Preprocessing: Thorough cleaning and handling of missing values to ensure the uniformity and reliability of the dataset.
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Exploratory Data Analysis (EDA): Visualizations and analyses providing insights into the dataset's composition, including class distribution, word clouds, and other patterns.
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Vectorization: Transforming textual data into a numerical format using Term Frequency-Inverse Document Frequency (TF-IDF) vectorization.
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Classification Models: Implementation of multiple models, including Logistic Regression, Naive Bayes, Random Forest, and Support Vector Machines (SVM), to predict the authenticity of news articles.
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Prediction and Analysis: Utilizing sentiment analysis as the predictive task and evaluating model performance through visualizations such as ROC curves and confusion matrices.
- Clone the repository:
git clone https://github.com/your-username/fake-news-detection.git